NEWS IV: a model with news and implied volatility for enhanced volatility prediction - Gentini, Vítor (2025)
This research explores how news and implied volatility (IV) influence volatility predictions for the Ibovespa index and five widely traded Brazilian stocks. We proposed a model which integrates IV and LDA-derived news topics and employed random forest to tackle nonlinearities and high dimensionality. Our results indicate that a model relying only on external variables (IV and news topics) significantly improves forecast accuracy for horizons beyond one day compared to the autoregressive models, even when these accounting for asymmetries and discontinuities. Our analysis of variable importance demonstrates that news topics are crucial for long-term forecasts, while IV significantly influences short-term predictions. This dissertation contributes to the economic literature by underlining the importance of textual data analysis and IV in volatility forecasting.